CityPulse: Large-scale data analytics for smart cities
1
Payam BarnaghiInstitute for Communication Systems (ICS)University of SurreyGuildford, United Kingdom
Smart City Data
− Data is multi-modal and heterogeneous− Noisy and incomplete− Time and location dependent − Dynamic and varies in quality − Crowed sourced data can be unreliable − Requires (near-) real-time analysis− Privacy and security are important issues
− Data alone may not give a clear picture -we need contextual information, background knowledge, multi-source information and obviously better data analytics solutions…
2
Smart City Data
3
?
What happens if we only focus on data
− Number of burgers consumed per day.− Number of cats outside.− Number of people checking their facebook
account.
− What insight would you draw?
4
What type of problems we expect to solve in
“smart” cities
Back to the future
6
7Source LAT Times, http://documents.latimes.com/la-2013/
Future cities: a view from 1998
8Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/
Source: wikipedia
9
The IoT and its applications
10
IoT
Diffusion of innovation
image source: Wikipedia
The Most Hyped Technology
image source: Forbes via Gartner
Moving fast forward
11
Source: AdamKR via Flicker, http://www.flickr.com/photos/adamkr/5045295251/in/photostream/
12
We need an Integrated Approach
13
CityPulse Consortium
Industrial SIE (Austria,
Romania), ERIC
SME AI
HigherEducation
UNIS, NUIG,UASO, WSU
City BR, AA
Partners:
Duration: 36 months
14
Processing steps
AnalyticsToolbox
Context-awareDecision Support,
Visualisation
Knowledge-based
Stream Processing
Real-TimeMonitoring &
Testing
Accuracy & Trust
Modelling
SemanticIntegration
On Demand Data
Federation
OpenReferenceData Sets
Real-TimeIoT InformationExtraction
IoT StreamProcessing
Federation ofHeterogenousData Streams
Design-Time Run-Time Testing
Exposure APIs
CityPulse – what we are going to deliver
...
Data Streams
Smart City Framework
Smart City Scenarios
a) Software tools/librariesin an integrated frameworkb) Back-end support servers
a) 101 scenariosb) 10 will be chosen to be prototyped
a) Data portals/ real-time access interfacesb) Interoperable formatsc) Common interfaces (REST/annotated)
a) Proof-of-Concepts and demonstrators and evaluations;Applications/Apps/Demos
Link: http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements
Stream Processing
...
Data Streams
CityPulse
Some of the key issues
− Data collection, representation, interoperability− Indexing, search and selection− Storage and provision − Stream analysis, fusion and integration of multi-source,
multi-modal and variable-quality data− Aggregation, abstraction, pattern extraction and
time/location dependencies − Adaptive learning models for dynamic data− Reasoning methods for uncertain and incomplete data− Privacy, trust, security− Scalability and flexibility of the solutions
17
Some of our recent in this domain
18
Use cases
Scenario ranking
101 Smart City Use-case Scenarios
http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements
101 Scenarios
− http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements
Data abstraction
23F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.
Ontology learning from real world data
24
Adaptable and dynamic learning methods
http://kat.ee.surrey.ac.uk/
Social media analysis (collaboration with Kno.e.sis, Wright State University)
26
City Infrastructure
Tweets from a city
P. Anantharam, P. Barnaghi, K. Thirunarayan, A. Sheth, "Extracting city events from social streams,“, under review, 2014.
https://osf.io/b4q2t/
Correlation analysis
27
28
Data analytics framework
29
Data:
Data
Domain
Knowledge
Socialsystems
InteractionsOpen Interfaces
Ambient
IntelligenceQuality and Trust
Privacy and
Security
Open Data
In Conclusion
− Smart cities are complex social systems and no technological and data- analytics-driven solution alone can solve the problems.
− Combination of data from Physical, Cyber and Social sources can give more complete, complementary data and contributes to better analysis and insights.
− Intelligent processing methods should be adaptable and handle dynamic, multi-modal, heterogeneous and noisy and incomplete data.
− Effective visualisation and interaction methods are also key to develop successful solutions.
− There are several solution for different parts of a data analytics framework in smart cities. An integrated approach is more effective in which IoT devices, communication networks, data analytics and learning algorithms and methods, services and interaction and visualistions and methods (and their optimisation algorithms) can work and cooperate together.
30
Q&A
− Thank you.
− EU FP7 CityPulse Project:
http://www.ict-citypulse.eu/
@ictcitypulse